





"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForMultipleChoice,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerModel,
       )
       from transformers.models.roformer.modeling_tf_roformer import (
           TFRoFormerSelfAttention,
           TFRoFormerSinusoidalPositionalEmbedding,
       )

class 						lowercase   :





      def __init__(			self ,		lowercase ,		lowercase=13 ,		lowercase=7 ,		lowercase=True ,		lowercase=True ,		lowercase=True ,		lowercase=True ,		lowercase=99 ,		lowercase=32 ,		lowercase=2 ,		lowercase=4 ,		lowercase=37 ,		lowercase="gelu" ,		lowercase=0.1 ,		lowercase=0.1 ,		lowercase=512 ,		lowercase=16 ,		lowercase=2 ,		lowercase=0.02 ,		lowercase=3 ,		lowercase=4 ,		lowercase=None ,		)						->	Dict:
            lowerCAmelCase    		=			parent
            lowerCAmelCase    		=			13
            lowerCAmelCase    		=			7
            lowerCAmelCase    		=			True
            lowerCAmelCase    		=			True
            lowerCAmelCase    		=			True
            lowerCAmelCase    		=			True
            lowerCAmelCase    		=			99
            lowerCAmelCase    		=			32
            lowerCAmelCase    		=			2
            lowerCAmelCase    		=			4
            lowerCAmelCase    		=			37
            lowerCAmelCase    		=			"""gelu"""
            lowerCAmelCase    		=			0.1
            lowerCAmelCase    		=			0.1
            lowerCAmelCase    		=			512
            lowerCAmelCase    		=			16
            lowerCAmelCase    		=			2
            lowerCAmelCase    		=			0.02
            lowerCAmelCase    		=			3
            lowerCAmelCase    		=			4
            lowerCAmelCase    		=			None





      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size					)

            lowerCAmelCase    		=			None
            if self.use_input_mask:
                  lowerCAmelCase    		=			random_attention_mask([self.batch_size, self.seq_length]					)

            lowerCAmelCase    		=			None
            if self.use_token_type_ids:
                  lowerCAmelCase    		=			ids_tensor([self.batch_size, self.seq_length] ,		self.type_vocab_size					)

            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			None
            if self.use_labels:
                  lowerCAmelCase    		=			ids_tensor([self.batch_size] ,		self.type_sequence_label_size					)
                  lowerCAmelCase    		=			ids_tensor([self.batch_size, self.seq_length] ,		self.num_labels					)
                  lowerCAmelCase    		=			ids_tensor([self.batch_size] ,		self.num_choices					)

            lowerCAmelCase    		=			RoFormerConfig(
                vocab_size=self.vocab_size ,		hidden_size=self.hidden_size ,		num_hidden_layers=self.num_hidden_layers ,		num_attention_heads=self.num_attention_heads ,		intermediate_size=self.intermediate_size ,		hidden_act=self.hidden_act ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		max_position_embeddings=self.max_position_embeddings ,		type_vocab_size=self.type_vocab_size ,		initializer_range=self.initializer_range ,		return_dict=lowercase ,		)

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			TFRoFormerModel(config=lowercase					)
            lowerCAmelCase    		=			{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

            lowerCAmelCase    		=			[input_ids, input_mask]
            lowerCAmelCase    		=			model(lowercase					)

            lowerCAmelCase    		=			model(lowercase					)

            self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			True
            lowerCAmelCase    		=			TFRoFormerForCausalLM(config=lowercase					)
            lowerCAmelCase    		=			{
                """input_ids""": input_ids,
                """attention_mask""": input_mask,
                """token_type_ids""": token_type_ids,
            }
            lowerCAmelCase    		=			model(lowercase					)["""logits"""]
            self.parent.assertListEqual(
                list(prediction_scores.numpy().shape					) ,		[self.batch_size, self.seq_length, self.vocab_size]					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	Tuple:
            lowerCAmelCase    		=			TFRoFormerForMaskedLM(config=lowercase					)
            lowerCAmelCase    		=			{
                """input_ids""": input_ids,
                """attention_mask""": input_mask,
                """token_type_ids""": token_type_ids,
            }
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.vocab_size)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			self.num_labels
            lowerCAmelCase    		=			TFRoFormerForSequenceClassification(config=lowercase					)
            lowerCAmelCase    		=			{
                """input_ids""": input_ids,
                """attention_mask""": input_mask,
                """token_type_ids""": token_type_ids,
            }

            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_labels)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	int:
            lowerCAmelCase    		=			self.num_choices
            lowerCAmelCase    		=			TFRoFormerForMultipleChoice(config=lowercase					)
            lowerCAmelCase    		=			tf.tile(tf.expand_dims(lowercase ,		1					) ,		(1, self.num_choices, 1)					)
            lowerCAmelCase    		=			tf.tile(tf.expand_dims(lowercase ,		1					) ,		(1, self.num_choices, 1)					)
            lowerCAmelCase    		=			tf.tile(tf.expand_dims(lowercase ,		1					) ,		(1, self.num_choices, 1)					)
            lowerCAmelCase    		=			{
                """input_ids""": multiple_choice_inputs_ids,
                """attention_mask""": multiple_choice_input_mask,
                """token_type_ids""": multiple_choice_token_type_ids,
            }
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_choices)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			self.num_labels
            lowerCAmelCase    		=			TFRoFormerForTokenClassification(config=lowercase					)
            lowerCAmelCase    		=			{
                """input_ids""": input_ids,
                """attention_mask""": input_mask,
                """token_type_ids""": token_type_ids,
            }
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.num_labels)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			TFRoFormerForQuestionAnswering(config=lowercase					)
            lowerCAmelCase    		=			{
                """input_ids""": input_ids,
                """attention_mask""": input_mask,
                """token_type_ids""": token_type_ids,
            }

            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(result.start_logits.shape ,		(self.batch_size, self.seq_length)					)
            self.parent.assertEqual(result.end_logits.shape ,		(self.batch_size, self.seq_length)					)





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase    		=			self.prepare_config_and_inputs()
            (
                (
                lowerCAmelCase
            )		,						(
                lowerCAmelCase
            )		,						(
                lowerCAmelCase
            )		,						(
                lowerCAmelCase
            )		,						(
                lowerCAmelCase
            )		,						(
                lowerCAmelCase
            )		,						(
                lowerCAmelCase
            )		,						
            )    		=			config_and_inputs
            lowerCAmelCase    		=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
            return config, inputs_dict







@require_tf
class 						lowercase   (   _UpperCAmelCase	,    _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = (
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE							      = (
          {
              'feature-extraction': TFRoFormerModel,
              'fill-mask': TFRoFormerForMaskedLM,
              'question-answering': TFRoFormerForQuestionAnswering,
              'text-classification': TFRoFormerForSequenceClassification,
              'text-generation': TFRoFormerForCausalLM,
              'token-classification': TFRoFormerForTokenClassification,
              'zero-shot': TFRoFormerForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	Optional[int]:
            if pipeline_test_casse_name == "TextGenerationPipelineTests":
                  return True

            return False





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			TFRoFormerModelTester(self					)
            lowerCAmelCase    		=			ConfigTester(self ,		config_class=lowercase ,		hidden_size=37					)





      def        _snake_case     (			self					)						->	Optional[int]:
            self.config_tester.run_common_tests()





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase					)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowercase					)





      def        _snake_case     (			self					)						->	str:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_lm_head(*lowercase					)





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*lowercase					)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*lowercase					)





      def        _snake_case     (			self					)						->	Optional[Any]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*lowercase					)





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*lowercase					)





      @slow
      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""					)
            self.assertIsNotNone(lowercase					)







@require_tf
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""					)
            lowerCAmelCase    		=			tf.constant([[0, 1, 2, 3, 4, 5]]					)
            lowerCAmelCase    		=			model(lowercase					)[0]

            # TODO Replace vocab size
            lowerCAmelCase    		=			50_000

            lowerCAmelCase    		=			[1, 6, vocab_size]
            self.assertEqual(output.shape ,		lowercase					)

            print(output[:, :3, :3]					)

            # TODO Replace values below with what was printed above.
            lowerCAmelCase    		=			tf.constant(
                [
                    [
                        [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                        [-1.5_133_783, 0.197_433, 0.15_190_607],
                        [-5.0_135_403, -3.900_256, -0.84_038_764],
                    ]
                ]					)
            tf.debugging.assert_near(output[:, :3, :3] ,		lowercase ,		atol=1e-4					)







@require_tf
class 						lowercase   (   unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = 1E-4





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			tf.constant([[4, 10]]					)
            lowerCAmelCase    		=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 ,		embedding_dim=6					)

            lowerCAmelCase    		=			emba(input_ids.shape					)
            lowerCAmelCase    		=			tf.constant(
                [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]					)

            tf.debugging.assert_near(lowercase ,		lowercase ,		atol=self.tolerance					)





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			tf.constant(
                [
                    [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
                    [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
                    [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
                ]					)
            lowerCAmelCase    		=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 ,		embedding_dim=512					)
            emba([2, 16, 512]					)
            lowerCAmelCase    		=			emba.weight[:3, :5]

            tf.debugging.assert_near(lowercase ,		lowercase ,		atol=self.tolerance					)







@require_tf
class 						lowercase   (   unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = 1E-4





      def        _snake_case     (			self					)						->	List[str]:
            # 2,12,16,64
            lowerCAmelCase    		=			tf.reshape(tf.range(2 * 12 * 16 * 64 ,		dtype=tf.floataa					) ,		shape=(2, 12, 16, 64)					) / 100

            lowerCAmelCase    		=			-tf.reshape(tf.range(2 * 12 * 16 * 64 ,		dtype=tf.floataa					) ,		shape=(2, 12, 16, 64)					) / 100

            lowerCAmelCase    		=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 ,		embedding_dim=64					)
            lowerCAmelCase    		=			embed_positions([2, 16, 768]					)[None, None, :, :]

            lowerCAmelCase		,						lowerCAmelCase    		=			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
                lowercase ,		lowercase ,		lowercase					)

            lowerCAmelCase    		=			tf.constant(
                [
                    [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
                    [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
                    [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
                    [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
                    [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
                    [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
                ]					)
            lowerCAmelCase    		=			tf.constant(
                [
                    [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
                    [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
                    [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
                    [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
                    [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
                    [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
                ]					)

            tf.debugging.assert_near(query_layer[0, 0, :6, :8] ,		lowercase ,		atol=self.tolerance					)
            tf.debugging.assert_near(key_layer[0, 0, :6, :8] ,		lowercase ,		atol=self.tolerance					)













"""simple docstring"""





from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






